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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2010"

共 976 条记录，以下是201-210 订阅

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Sparse Reconstruction Of Equivalence Classes Of Moving targets Using Single-Channel Synthetic Aperture Radar

Sparse Reconstruction Of Equivalence Classes Of Moving Targe...

引用

IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Gunther, Jake Hunsaker, Josh Anderson, Hyrum Moon, Todd Utah State Univ Elect & Comp Engn Logan UT 84322 USA Sandia Natl Labs Data Anal & Data Exploitat Albuquerque NM 87185 USA

ISBN: (纸本)9781479928934

Simultaneously estimating position and velocity of moving targets using only phase information from single-channel SAR data is impossible. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. We present a definitions for endo- and exo-clutter that is consistent with the equivalence classes, and show that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.

关键词： Radar signal processing synthetic aperture radar motion detection sparse signal recovery

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Detection of small targets and their Characterization Based on their Formation using an Image Feature Network-Based Object Recognition Algorithm

Detection of Small Targets and their Characterization Based ...

引用

conference on signal and data processing of small targets

作者： Straub, Jeremy Univ N Dakota Dept Comp Sci Grand Forks ND 58202 USA

ISBN: (纸本)9781628410297

small target detection and classification is problematic. For targets that operate as part of a cluster, classification can be performed based on the characteristics of the cluster's operations, instead of trying to identify an individual cluster-member directly. This paper presents an algorithm for object identification based on comparing networks of point-to-point distances between features identified by an image feature detection algorithm. It discusses the alterations required to make the algorithm suitable for performing cluster-formation based characterization of small targets from point or near-point source data. An analysis of the algorithm's performance is presented and it efficacy for this application assessed.

关键词： small target detection and classification point targets classification decision cluster object identification cluster-formation based characterization of small targets

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small TARGET OF SEA SURFACE DETECTION ALGORITHM RESEARCH BASED ON FEATURE CLASSIFICATION 12

SMALL TARGET OF SEA SURFACE DETECTION ALGORITHM RESEARCH BAS...

引用

12th IEEE International conference on signal processing (ICSP)

作者： Yu, Lianqing Li, Yun Wang, Ning Jiang, Jin Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China

ISBN: (纸本)9781479921867

This paper focuses on small target of sea surface detection in sea clutter. Firstly, it analyses the statistic of sea clutter. Secondly, three feature vectors (namely, RAA, RPH and RVE) are extracted, which is utilized to discriminate the sea clutter and targets, and then the time-Doppler curve is used to distinguish the multi-targets in order to finish the small target of sea surface detection. Finally, it analyses the trial data of some radar and shows that this algorithm can effectively distinguish the sea clutter and small target of sea surface. This paper provides a new approach for the ship-borne surveillance radar or marine radar to detect the small target of sea surface and antisubmarine warfare patrol.

关键词： Sea Cluttert Target Detection Feature Detection Feature Classification Time-Doppler Curve

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Tracking Low SNR targets Using Particle Filter with Flow Control

Tracking Low SNR Targets Using Particle Filter with Flow Con...

引用

conference on signal and data processing of small targets

作者： Moshtagh, Nima Romberg, Paul M. Chan, Moses W. Lockheed Martin Space Syst Co Adv Technol Ctr Sunnyvale CA 94089 USA

ISBN: (纸本)9781628410297

In this work we study the problem of detecting and tracking challenging targets that exhibit low signal-to-noise ratios (SNR). We have developed a particle filter-based track-before-detect (TBD) algorithm for tracking such dim targets. The approach incorporates the most recent state estimates to control the particle flow accounting for target dynamics. The flow control enables accumulation of signal information over time to compensate for target motion. The performance of this approach is evaluated using a sensitivity analysis based on varying target speed and SNR values. This analysis was conducted using high-fidelity sensor and target modeling in realistic scenarios. Our results show that the proposed TBD algorithm is capable of tracking targets in cluttered images with SNR values much less than one.

关键词： Particle Filtering Dim Target Tracking Track-Before-Detect Algorithms

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Application of Rich Feature Descriptors to small Target Detection in Wide-Area Persistent ISR Systems

Application of Rich Feature Descriptors to Small Target Dete...

引用

conference on signal and data processing of small targets

作者： Miller, Christopher W. Edelberg, Jason. A. Wilson, Michael. L. Novak, Kyle. M. US Naval Res Lab Code 56644555 Overlook Ave SW Washington DC 20375 USA Tekla Res Dumfires VA USA

ISBN: (纸本)9781628410297

One of the desired capabilities for wide-area persistent ISR systems is to reliably locate and subsequently track the movement of targets within the field of view. Current wide-area persistent ISR systems are characterized by large pixel overall counts and very large fields of view. This leads to a large ground sample distance with few pixels-on-target. Locating targets under these constraints is extremely difficult due to the fact that the targets present very little detailed structure. In this paper we will present the application of rich image feature descriptors combined with advanced statistical target detection methodologies to the airborne ISR problem. We will demonstrate that these algorithms can reliably locate targets in the scene without relying on the target's motion to form a detection. This is useful in ISR application where it is desirable to be able to continuously track a target through stops and maneuvers.

关键词： Wide area surveillance automated target recognition support vector machines

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CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOWN NON-targets FOR PLDA-SVM SCORING

CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOW...

引用

IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Rao, Wei Mak, Man-Wai Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479928934

Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers and claimants only. We have previously demonstrated that better performance can be achieved by incorporating the information of background speakers in the scoring process via speaker-dependent SVMs. This is achieved by defining a PLDA score space with dimension equal to the number of training i-vectors for each target speaker. The new protocol in NIST 2012 SRE permits systems to use the information of other target-speakers (called known non-targets) in each verification trial. In this paper, we exploit this new protocol to enhance the performance of PLDA-SVM scoring by using the score vectors of both known and unknown non-targets as the impostor class data to train the speaker-dependent SVMs. Because some target speakers have one enrollment utterance only, which results in severe imbalance in the speaker-and impostor-class data for SVM training. This paper shows that if the enrollment utterance is sufficiently long, a number of target-speaker i-vectors can be generated by an utterance partitioning and resampling technique, resulting in much better scoring SVMs. Results on NIST 2012 SRE demonstrate the advantages of pooling the known and unknown non-targets for training the SVMs and that the resampling techniques can help the SVM training algorithm to find better decision boundaries for those speakers with only a small number of enrollment utterances.

关键词： I-vectors probabilistic linear discriminant analysis empirical kernel maps likelihood ratio kernels NIST 2012 SRE

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Approximate Calculation of Marginal Association Probabilities using a Hybrid data Association Model

Approximate Calculation of Marginal Association Probabilitie...

引用

conference on signal and data processing of small targets

作者： Baum, Marcus Willett, Peter Bar-Shalom, Yaakov Hanebeck, Uwe D. Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781628410297

The calculation of marginal association probabilities is the major computational bottleneck in the Joint Probabilistic data Association Filter (JPDAF). In this paper, we investigate approximations for the marginal associations that simplify the (computational complex) original association model in order to obtain efficient algorithms. In this context, we first discuss the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated Probabilistic data Association (LMIPDA) algorithm. Second, we propose a fast novel approximation that exploits systematic combinations of the JPDAF measurement model with the Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model. The discussed methods are evaluated by means of a tracking scenario with a high number of closely-spaced targets.

关键词： Joint Probabilistic data Association Filter (JPDAF) marginal association probabilities Probabilistic Multi-Hypothesis Tracker (PMHT)

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A Novel signal Reconstructing Method for Radar targets 2

A Novel Signal Reconstructing Method for Radar Targets

引用

2nd IEEE China Summit / International conference on signal and Information processing (IEEE ChinaSIP)

作者： Duan, Jia Zhang, Lei Wu, Yifeng Xing, Mengdao Wu, Min Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781479954032

In this paper, a novel signal reconstructing method for radar targets is proposed based on the attributed scattering center model. By extracting the attributed parameters, the large amount of target data can be represented by small amounts of attributed parameters. In this way, the data amount has been compressed sharply, which releases the computer memory for storage. After extraction, a target discriminating method is presented by applying a CFAR threshold to the energy of extracted attributed scattering centers, by which, weak distributed scattering centers with relatively high energy in total can be discriminated from noise under low SNRs. Experimental results validate the effectiveness of the signal reconstructing capability of the proposal.

关键词： signal reconstruction attributed scattering center radar images

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Advanced EMI Models for Survey data processing: targets Detection and Classification

Advanced EMI Models for Survey Data Processing: Targets Dete...

引用

conference on Detection and Sensing of Mines, Explosive Objects, and Obscured targets XIX

作者： Shubitidze, F. Sigman, J. B. Wang, Y. Miller, J. Keranen, J. Shamatava, I. Barrowes, B. E. O'Neill, K. Dartmouth Coll Thayer Sch Engn HB 8000 Hanover NH 03755 USA White River Technol Inc Etna NH 03750 USA CUSA ERDC Cold Regions Res & Engn Lab Hanover NH 03755 USA

ISBN: (纸本)9781628410099

One of the most challenging aspects of survey data processing is target selection. The fundamental input for the classification is dynamic data collected along survey lines. These data are different from the static data obtained in cued mode and used for target classification. Survey data are typically collected using just one transmitter loop (the Z-axis loop) and feature short data point collection times and short decay transience. The collection intervals for each data point are typically 0.1 s, and the signal repetition rates are typically 90 or 270 Hz (in other words, the transient decay times are 2.7 ms or 0.9 ms). Reliable classification requires multiple side/angle illumination;i.e., to conduct reliable classification it is necessary to combine and jointly invert multiple data points. However, picking data points that provide optimal information for classifying targets is a difficult task. The traditional method plots signal amplitudes on a 2D map and picks peaks of signal level without properly accounting for the underlying physics. In this paper, the joint diagonalization is applied to survey data sets to improve data pre-processing and target picking The JD technique is an EMI data analysis and target classification technique and is applicable for all next-generation multi-static array EMI sensors. The method extracts multi-static response data matrix eigenvalues. The eigenvalues are main characteristics of the data. Recent studies have demonstrated that the method has great potential to quickly estimate the number of potential targets and moreover classify these targets at the data pre-processing stage, in real time and without the need for a forward model. Another advantage of JD is that it provides the ability to separate signal from noise making it possible to de-noise data without distorting the signal due to the targets. In this paper the JD technique is used to process dynamic data collected at South West Proving Ground and Aberdeen Proving Ground

关键词： Dynamic systems OPTEMA UXO sensors small targets advanced models classification discrimination ONVMS modeling data collection deep targets

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Dual-Sided Subspace Mappings for Main-Beam Multi-Target Super-Resolution in Clutter and Interference

Dual-Sided Subspace Mappings for Main-Beam Multi-Target Supe...

引用

IEEE Radar conference - From Sensing to Information

作者： Fernandez, Manuel F. Yu, Kai-Bor Independent Researcher Syracuse NY USA

ISBN: (纸本)9781479920358

The need for localizing multiple main-beam targets in the presence of interference, clutter and jammers arises in scenarios such as: air defense, where a radar detection may correspond to one or multiple targets (e.g., a missile launched from an airborne platform);ballistic missile defense, where the incoming missile complex involves a large number of objects;cruise missile defense for low angle target tracking in multipath;etc. This paper presents a super-resolution approach for the sub-beamwidth localization of multiple main-beam targets in the presence of clutter and interference, doing so with a single snapshot of sensor array data while placing nulls at specified locations. Such process first maps array data into compact subspaces containing the information of interest (e.g., a cluster of receive beams with imbedded nulls). Super-resolution is then achieved via small-matrix operations on the subspace data. The ensuing result is a mapping of array data into a compressed "information domain," yielding an effective, practical Sensor-to-Information process that is more accurate, robust and versatile than current super-resolution methods.

关键词： Array signal processing Arrays Clutter Noise signal resolution Vectors

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